In recent years, the impulsive population systems have been studied by many researchers. However, seasonal effects on prey are rarely discussed. Thus, in this paper, the dynamics of the Holling-type IV two-competitive-prey one-predator system with impulsive perturbations and seasonal effects are analyzed using the Floquet theory and comparison techniques. It is assumed that the impulsive pertur...

Theory of impulsive partial differential equations can be applied to many fields, such as to biology, population growth, engineering, generic repression, control theory and climate model. In the last few years, the fundamental theory of partial differential equations with impulses has undergone intensive development. The qualitative theory of this class of equations, however, is still in an ini...

Observations are presented of short-lived, highly structured bursts of rapid plasma flow observed with the ElSCAT radar in the high latitude dayside ionosphere. It is shown that the properties of the bursts are consistent with ionospheric perturbations caused by impulsive, localized reconnection at the Earth's magnetopause, i.e. by flux

We consider a certain type of second-order neutral delay differential systems and we establish two results concerning the oscillation of solutions after the system undergoes controlled abrupt perturbations (called impulses). As a matter of fact, some particular non-impulsive cases of the system are oscillatory already. Thus, we are interested in finding adequate impulse controls under which our...

Keywords: Predator–prey model Impulsive state feedback control Periodic solution Stability Bifurcation a b s t r a c t In the paper, we consider a prey-dependent model with integrated pest management strategies, that is, when the pest population reaches the economic injury level, we use a combination tactics such as biological, cultural, and chemical control tactics such that pests reduce to a ...

and Applied Analysis 3 In the practical application, a neural system is affected by external perturbations. It is necessary to consider the effect of randomness to neural networks. We obtain stochastic impulsive neural networks with delays: dy (t) = [ − A 0 y (t) + A 1 g (y (t)) + A 2 g (y (t − τ (t)))